The present invention relates generally to an Inductive-Deductive Process Design for Machined Parts.
In the machining of discrete mechanical parts using milling and drilling operations, four levels of process design must be addressed: Level 1 is the grouping o features into setups to be sequenced to machine (rough and finish) the part design, Level 2 is the selection of fixtures needed to position and secure the starting stock of material during a setup, Level 3 is the sequencing of shape features and Level 4 is the sequencing of operations across features within a set up.
For such complex geometries, an experienced machinist/process designer can, nevertheless, come up with a suitable design by evaluating alternative process designs. The machinist/process designer recursively iterates on the selection of fixturing, setup and feature sequencing alternatives until a process design is selected. The proper design and fabrication of such a process is a difficult enterprise normally undertaken only by highly experienced machinists/process designers, especially if the part geometry is truly complex.
A metal part, regardless of the shape complexity and forming process, typically requires some material removal or finishing process involving drilling or milling, i.e., machining. Although machining processes are separate and distinct from the other process steps, they are highly dependent on pre and post processes such as forging and casting or plating, heat treating, grinding and inspection. Because of the amount of time associated with machining a part (i.e., hours or even days), the process is often viewed as a discrete versus a continuous problem. The discrete nature of the problem does not easily lend itself to solution via a mathematical expression although various dynamic, branch and bound, and linear programming techniques have been employed in the past. Typically these techniques attempt to simplify the problem to conform to their computational limitations and very often are not utilized in practice.
An optimal process design for a particular part may be 40 hours and the next best design may be 100 hours, depending on alternative setups, fixtures, cutting tools and machines. The difference is very often dependent on the experience of the machinist in either recalling the process design of a similar part in the past or associating general constraints which apply to this particular part design and enable the generation of a new process design, together with the associated selection of fixtures, setups, machines and tools. Although various programming techniques can help organize the problem, the various and competing interdependencies (e.g., minimum tool changes, minimum setups, minimum machine travel distance combined with overriding safety and quality constraints) between variables severely limits these other techniques.
Other than manual trial and error as practiced by the machinist/process designers today, to our knowledge there exists no prior means to self-improve process design for machining. Although various group technology process planning systems (both generative and variant) and recent `disassembly` algorithms automate aspects of process design for machining, none of them are capable of multi-objective optimization of setups, fixtures, features and operations involving material, geometric and processing resource constraints.